Data

This course covers commonly used statistical inference methods for numerical and categorical data.

In this course, you will learn the landscape of relevant systems, the principles on which they rely, their tradeoffs, and how to evaluate their utility against your requirements. You will learn how practical systems were derived from the frontier of research in computer science and what systems are coming on the horizon. Cloud computing, SQL and NoSQL databases, MapReduce and the ecosystem it spawned, Spark and its contemporaries, and specialized systems for graphs and arrays will be covered.

Improve your Data Handling skills to an outstanding level. Pandas fully explained. Real Data. 150+ Exercises. Must-have skills for Data Science & Finance. Seaborn & Time Series.

Learn how to create data models, relationships and use DAX formulas in Microsoft Power BI

By proactively dealing with privacy issues, organizations can safely leverage Big Data while still retaining customers, and avoiding reputational harm, litigation, and regulatory scrutiny. In this course, you will examine privacy concerns, how data can be used ethically, and what to do about social media.

Big data is a term for data sets so large that traditional data processing applications can not be used to perform any sort of analysis. It is often semi structured or unstructured in form. There are a number of unique challenges that arise when companies begin to use big data. The least of which are the engineering concerns. This course will introduce some of those engineering challenges and describe how some companies have come up with solutions.

Big data dramatically impacts all aspects of business culture. Companies need to evolve from their traditional methods and practices to be able to use big data to improve their organization. In this course we will examine the impacts of big data from the marketing perspective. we will look at how the mobile effect has changed marketing, how purchasing habits have changed and what impact datafying consumer behavior has done.

Big Data allows salespeople to adopt data-driven methodologies to target high-value prospects rather than relying on relationships and other soft factors to target and close business deals. In this course, you will learn the difference between big data and data science. You will take a look at different algorithms and technology accelerators.

This course is for those new to data science and interested in understanding why the Big Data Era has come to be. It is for those who want to become conversant with the terminology and the core concepts behind big data problems, applications, and systems. It is for those who want to start thinking about how Big Data might be useful in their business or career. It provides an introduction to one of the most common frameworks, Hadoop, that has made big data analysis easier and more accessible.

Taught by a team which includes 2 Stanford-educated, ex-Googlers and 2 ex-Flipkart Lead Analysts. This team has decades of practical experience in working with large-scale data processing.

Has your data gotten huge, unwieldy and hard to manage with a traditional database? Is your data unstructured with an expanding list of attributes? Do you want to ensure your data is always available even with server crashes? Look beyond Hadoop - the Cassandra distributed database is the solution to your problems.

Hadoop, MapReduce, HDFS, Spark, Pig, Hive, HBase, MongoDB, Cassandra, Flume - the list goes on! Over 25 technologies.

“Big data" analysis is a hot and highly valuable skill – and this course will teach you the hottest technology in big data: Apache Spark. Employers including Amazon, EBay, NASA JPL, and Yahoo all use Spark to quickly extract meaning from massive data sets across a fault-tolerant Hadoop cluster. You'll learn those same techniques, using your own Windows system right at home. It's easier than you might think.

Learn and master the art of framing data analysis problems as Spark problems through over 15 hands-on examples, and then scale them up to run on cloud computing services in this course. You'll be learning from an ex-engineer and senior manager from Amazon and IMDb.

• Learn the concepts of Spark's Resilient Distributed Datastores

• Develop and run Spark jobs quickly using Python

• Translate complex analysis problems into iterative or multi-stage Spark scripts

• Scale up to larger data sets using Amazon's Elastic MapReduce service

• Understand how Hadoop YARN distributes Spark across computing clusters

• Learn about other Spark technologies, like Spark SQL, Spark Streaming, and GraphX

By the end of this course, you'll be running code that analyzes gigabytes worth of information – in the cloud – in a matter of minutes.

This course uses the familiar Python programming language; if you'd rather use Scala to get the best performance out of Spark, see my "Apache Spark with Scala - Hands On with Big Data" course instead.

The Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures. This course explains Oozie as a workflow tool used to manage multiple stage tasks in Hadoop. Additionally, you'll learn how to use Hue, a front end tool which is browser based.

The core of Hadoop consists of a storage part, HDFS, and a processing part, MapReduce. Hadoop splits files into large blocks and distributes the blocks amongst the nodes in the cluster. To process the data, Hadoop and MapReduce transfer code to nodes that have the required data, which the nodes then process in parallel. This approach takes advantage of data locality to allow the data to be processed faster and more efficiently via distributed processing than by using a more conventional supercomputer architecture that relies on a parallel file system where computation and data are connected via high-speed networking. In this course, you'll learn about the theory of YARN as a parallel processing framework for Hadoop. You'll also learn about the theory of MapReduce as the backbone of parallel processing jobs. Finally, this course demonstrates MapReduce in action by explaining the pertinent classes and then walk through a MapReduce program step by step.

Hadoop is an open source software project that enables distributed processing of large data sets across clusters of commodity servers. It is designed to scale up from a single server to thousands of machines, with very high degree of fault tolerance. Rather than relying on high-end hardware, the resiliency of these clusters comes from the software's ability to detect and handle failures at the application layer. In this course, you'll learn about the theory of Flume as a tool for dealing with extraction and loading of unstructured data. You'll explore a detailed explanation of the Flume agents and a demonstration of the Flume agents in action.

Hadoop is an open source Java framework for processing and querying vast amounts of data on large clusters of commodity hardware. It relies on an active community of contributors from all over the world for its success. In this course, you'll explore the server architecture for Hadoop and learn about the functions and configuration of the daemons making up the Hadoop Distributed File System. You'll also learn about the command line interface and common HDFS administration issues facing all end users. Finally, you'll explore the theory of HBase as another data repository built alongside or on top of HDFS, and basic HBase commands.

Hadoop is an open-source software framework for storing and processing big data in a distributed fashion on large clusters of commodity hardware. Essentially, it accomplishes two tasks: massive data storage and faster processing. This course explains the theory of Sqoop as a tool for dealing with extraction and loading of structured data from a RDBMS. You'll explore an explanation of Hive SQL statements and a demonstration of Hive in action.

The world of Hadoop and "Big Data" can be intimidating - hundreds of different technologies with cryptic names form the Hadoop ecosystem. With this course, you'll not only understand what those systems are and how they fit together - but you'll go hands-on and learn how to use them to solve real business problems! Learn and master the most popular big data technologies in this comprehensive course, taught by a former engineer and senior manager from Amazon and IMDb. We'll go way beyond Hadoop itself, and dive into all sorts of distributed systems you may need to integrate with.

Process Modeling Techniques for Requirements Elicitation and Workflow Analysis

Data sensitivity and security breaches are common in news media reports. Explore how a structured data access governance framework results in reducing the likelihood of data security breaches.

As industries, enterprises, and jobs become more data-intensive, data literacy is critical to effectively “talk data” with business colleagues and data and analytics professionals who support you in your work. This course covers fundamental concepts in data management, data quality, data privacy and protection, and data governance. This course was developed with subject matter provided by the International Institute for Analytics. (www.iianalytics.com)

Learn how to pique and keep your audience's attention so they will understand and remember your data presentation.

What you'll learn:

-Master the techniques needed to build data models for your organization.

-Apply key data modeling design principles through both classic entity-relationship notation and the “crow’s foot” notation.

- Build semantically accurate data models consisting of entities, attributes, relationships, hierarchies, and other modeling constructs.

-Convert conceptual data models to logical and physical data models through forward engineering.

Requirements

•Students only need a basic understanding of data management concepts and constructs such as relational database tables and how different pieces of data logically relate to one another. The course content builds on these rudimentary ideas; no other prerequisites are needed.

Analyze data with Power BI, explore dashboards, reports and apps